Analysis of GLDS-38 from NASA GeneLab
This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven Xijin.Ge@sdstate.edu
Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491
First we set up the working directory to where the files are saved.
input_biclustMethod_ <- "BCCC()"
R packages used
library(RSQLite, verbose = FALSE) # for database connection
library(gplots, verbose = FALSE) # for hierarchical clustering
library(ggplot2, verbose = FALSE) # graphics
library(e1071, verbose = FALSE) # computing kurtosis
#library(DT, verbose = FALSE) # for renderDataTable
library(plotly, verbose = FALSE) # for interactive heatmap
library(reshape2, verbose = FALSE) # for melt correlation matrix in heatmap
# From Data Read Function
library(edgeR, verbose = FALSE) # count data D.E.
library(DESeq2, verbose = FALSE) # count data analysis, DEG.DESeq2
# TSNE Plot, tSNEgenePlot
library(Rtsne, verbose = FALSE)
# PGSA Pathway PGSEA Pathway, PGSEAplot
library(PGSEA, verbose = FALSE)
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
## version 3.12
# DEG.limma
library(limma, verbose = FALSE) # Differential expression
library(statmod, verbose = FALSE)
# enrichment plot
library(dendextend) # customizing tree
# enrich.net2, moduleNetwork
library(igraph)
# Stringdb_geneList, StringDB_GO_enrichmentData, stringDB_network1
# StringDB_network_link
library(STRINGdb, verbose = FALSE)
# gagePathwayData
library(gage, verbose = FALSE) # pathway analysis
# fgseaPathwayData
library(fgsea, verbose = FALSE) # fast GSEA
# ReactomePAPathwayData
library(ReactomePA, verbose = FALSE) # pathway analysis
# KeggImage
library(pathview)
# genomePlot, genomePlotDataPre
library(PREDA, verbose = FALSE) # showing expression on genome
library(PREDAsampledata, verbose = FALSE)
library(hgu133plus2.db, verbose = FALSE)
# biclustering
library(biclust, verbose = FALSE)
library(knitr) # install if needed. for showing tables with kable
library(kableExtra)
if (input_biclustMethod_ == "BCQU()") {
library(QUBIC, verbose = FALSE)
} # have trouble installing on Linux
if (input_biclustMethod_ == "BCUnibic()") {
library(runibic, verbose = FALSE)
} # Test biclustMethod dependant qubic runibic
# wgcna
library(WGCNA)
library(flashClust, verbose = FALSE)
source("iDEP_core_functions_only.R")
# Each row of this matrix represents a color scheme;
mycolors_ <- sort(rainbow(20))[c(1, 20, 10, 11, 2, 19, 3, 12, 4, 13, 5, 14, 6, 15, 7, 16, 8, 17, 9, 18)]
hmcols_ <- colorRampPalette(colors = c('#4575B4', '#91BFDB', '#E0F3F8', '#FFFFBF', '#FEE090', '#FC8D59', '#D73027'))(75)
heatColors_ <- rbind(
greenred(75),
bluered(75),
colorpanel(75, "green", "black", "magenta"),
colorpanel(75, "blue", "yellow", "red"),
hmcols_
)
rownames(heatColors_) <- c("Green-Black-Red", "Blue-White-Red", "Green-Black-Magenta", "Blue-Yellow-Red", "Blue-white-brown")
We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).
inputFolderFiles <- list.files(params$input_folder, full.names = TRUE)
inputFile_ <- inputFolderFiles[stringr::str_detect(tolower(inputFolderFiles), "expression.csv")]
sampleInfoFile_ <- inputFolderFiles[stringr::str_detect(tolower(inputFolderFiles), "sampleinfo.csv")]
gldsMetadataFile_ <- inputFolderFiles[stringr::str_detect(tolower(inputFolderFiles), "metadata.csv")]
geneInfoFile_ <- params$geneInfoFile
geneSetFile_ <- params$geneSetFile # pathway database in SQL; can be GMT format
STRING10_speciesFile_ <- "https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv"
readMetadata.out_ <- readMetadata(inFile = gldsMetadataFile_) #gldsMetadataFile_)
kable(readMetadata.out_) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Col0_GC_Alight_Rep1 | Col0_GC_Alight_Rep2 | Col0_GC_Alight_Rep3 | Col0_GC_dark_Rep1 | Col0_GC_dark_Rep2 | Col0_GC_dark_Rep3 | Ws_GC_Alight_Rep1 | Ws_GC_Alight_Rep2 | Ws_GC_Alight_Rep3 | Ws_GC_dark_Rep1 | Ws_GC_dark_Rep2 | Ws_GC_dark_Rep3 | Col0PhyD_GC_Alight_Rep1 | Col0PhyD_GC_Alight_Rep2 | Col0PhyD_GC_Alight_Rep3 | Col0PhyD_GC_dark_Rep1 | Col0PhyD_GC_dark_Rep2 | Col0PhyD_GC_dark_Rep3 | Col0_FLT_Alight_Rep1 | Col0_FLT_Alight_Rep2 | Col0_FLT_Alight_Rep3 | Col0_FLT_dark_Rep1 | Col0_FLT_dark_Rep2 | Col0_FLT_dark_Rep3 | Ws_FLT_Alight_Rep1 | Ws_FLT_Alight_Rep2 | Ws_FLT_Alight_Rep3 | Ws_FLT_dark_Rep1 | Ws_FLT_dark_Rep2 | Ws_FLT_dark_Rep3 | Col0PhyD_FLT_Alight_Rep1 | Col0PhyD_FLT_Alight_Rep2 | Col0PhyD_FLT_Alight_Rep3 | Col0PhyD_FLT_dark_Rep1 | Col0PhyD_FLT_dark_Rep2 | Col0PhyD_FLT_dark_Rep3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sample.LongId | Atha.Col.0.root.GC.Alight.Rep1.GSM2493759.RNAseq.RNAseq | Atha.Col.0.root.GC.Alight.Rep2.GSM2493760.RNAseq.RNAseq | Atha.Col.0.root.GC.Alight.Rep3.GSM2493761.RNAseq.RNAseq | Atha.Col.0.root.GC.dark.Rep1.GSM2493768.RNAseq.RNAseq | Atha.Col.0.root.GC.dark.Rep2.GSM2493769.RNAseq.RNAseq | Atha.Col.0.root.GC.dark.Rep3.GSM2493770.RNAseq.RNAseq | Atha.Ws.root.GC.Alight.Rep1.GSM2493762.RNAseq.RNAseq | Atha.Ws.root.GC.Alight.Rep2.GSM2493763.RNAseq.RNAseq | Atha.Ws.root.GC.Alight.Rep3.GSM2493764.RNAseq.RNAseq | Atha.Ws.root.GC.dark.Rep1.GSM2493771.RNAseq.RNAseq | Atha.Ws.root.GC.dark.Rep2.GSM2493772.RNAseq.RNAseq | Atha.Ws.root.GC.dark.Rep3.GSM2493773.RNAseq.RNAseq | Atha.Col.0.PhyD.root.GC.Alight.Rep1.GSM2493765.RNAseq.RNAseq | Atha.Col.0.PhyD.root.GC.Alight.Rep2.GSM2493766.RNAseq.RNAseq | Atha.Col.0.PhyD.root.GC.Alight.Rep3.GSM2493767.RNAseq.RNAseq | Atha.Col.0.PhyD.root.GC.dark.Rep1.GSM2493774.RNAseq.RNAseq | Atha.Col.0.PhyD.root.GC.dark.Rep2.GSM2493775.RNAseq.RNAseq | Atha.Col.0.PhyD.root.GC.dark.Rep3.GSM2493776.RNAseq.RNAseq | Atha.Col.0.root.FLT.Alight.Rep1.GSM2493777.RNAseq.RNAseq | Atha.Col.0.root.FLT.Alight.Rep2.GSM2493778.RNAseq.RNAseq | Atha.Col.0.root.FLT.Alight.Rep3.GSM2493779.RNAseq.RNAseq | Atha.Col.0.root.FLT.dark.Rep1.GSM2493786.RNAseq.RNAseq | Atha.Col.0.root.FLT.dark.Rep2.GSM2493787.RNAseq.RNAseq | Atha.Col.0.root.FLT.dark.Rep3.GSM2493788.RNAseq.RNAseq | Atha.Ws.root.FLT.Alight.Rep1.GSM2493780.RNAseq.RNAseq | Atha.Ws.root.FLT.Alight.Rep2.GSM2493781.RNAseq.RNAseq | Atha.Ws.root.FLT.Alight.Rep3.GSM2493782.RNAseq.RNAseq | Atha.Ws.root.FLT.dark.Rep1.GSM2493789.RNAseq.RNAseq | Atha.Ws.root.FLT.dark.Rep2.GSM2493790.RNAseq.RNAseq | Atha.Ws.root.FLT.dark.Rep3.GSM2493791.RNAseq.RNAseq | Atha.Col.0.PhyD.root.FLT.Alight.Rep1.GSM2493783.RNAseq.RNAseq | Atha.Col.0.PhyD.root.FLT.Alight.Rep2.GSM2493784.RNAseq.RNAseq | Atha.Col.0.PhyD.root.FLT.Alight.Rep3.GSM2493785.RNAseq.RNAseq | Atha.Col.0.PhyD.root.FLT.dark.Rep1.GSM2493792.RNAseq.RNAseq | Atha.Col.0.PhyD.root.FLT.dark.Rep2.GSM2493793.RNAseq.RNAseq | Atha.Col.0.PhyD.root.FLT.dark.Rep3.GSM2493794.RNAseq.RNAseq |
| Sample.Id | ||||||||||||||||||||||||||||||||||||
| Sample.Name | Atha_Col-0_root_GC_Alight_Rep1_GSM2493759 | Atha_Col-0_root_GC_Alight_Rep2_GSM2493760 | Atha_Col-0_root_GC_Alight_Rep3_GSM2493761 | Atha_Col-0_root_GC_dark_Rep1_GSM2493768 | Atha_Col-0_root_GC_dark_Rep2_GSM2493769 | Atha_Col-0_root_GC_dark_Rep3_GSM2493770 | Atha_Ws_root_GC_Alight_Rep1_GSM2493762 | Atha_Ws_root_GC_Alight_Rep2_GSM2493763 | Atha_Ws_root_GC_Alight_Rep3_GSM2493764 | Atha_Ws_root_GC_dark_Rep1_GSM2493771 | Atha_Ws_root_GC_dark_Rep2_GSM2493772 | Atha_Ws_root_GC_dark_Rep3_GSM2493773 | Atha_Col-0-PhyD_root_GC_Alight_Rep1_GSM2493765 | Atha_Col-0-PhyD_root_GC_Alight_Rep2_GSM2493766 | Atha_Col-0-PhyD_root_GC_Alight_Rep3_GSM2493767 | Atha_Col-0-PhyD_root_GC_dark_Rep1_GSM2493774 | Atha_Col-0-PhyD_root_GC_dark_Rep2_GSM2493775 | Atha_Col-0-PhyD_root_GC_dark_Rep3_GSM2493776 | Atha_Col-0_root_FLT_Alight_Rep1_GSM2493777 | Atha_Col-0_root_FLT_Alight_Rep2_GSM2493778 | Atha_Col-0_root_FLT_Alight_Rep3_GSM2493779 | Atha_Col-0_root_FLT_dark_Rep1_GSM2493786 | Atha_Col-0_root_FLT_dark_Rep2_GSM2493787 | Atha_Col-0_root_FLT_dark_Rep3_GSM2493788 | Atha_Ws_root_FLT_Alight_Rep1_GSM2493780 | Atha_Ws_root_FLT_Alight_Rep2_GSM2493781 | Atha_Ws_root_FLT_Alight_Rep3_GSM2493782 | Atha_Ws_root_FLT_dark_Rep1_GSM2493789 | Atha_Ws_root_FLT_dark_Rep2_GSM2493790 | Atha_Ws_root_FLT_dark_Rep3_GSM2493791 | Atha_Col-0-PhyD_root_FLT_Alight_Rep1_GSM2493783 | Atha_Col-0-PhyD_root_FLT_Alight_Rep2_GSM2493784 | Atha_Col-0-PhyD_root_FLT_Alight_Rep3_GSM2493785 | Atha_Col-0-PhyD_root_FLT_dark_Rep1_GSM2493792 | Atha_Col-0-PhyD_root_FLT_dark_Rep2_GSM2493793 | Atha_Col-0-PhyD_root_FLT_dark_Rep3_GSM2493794 |
| GLDS | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 | 120 |
| Accession | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 | GLDS-120 |
| Hardware | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish | Petri dish |
| Tissue | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots | Roots |
| Age | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days | 11 days |
| Organism | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana | Arabidopsis thaliana |
| Ecotype | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | WS-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 | Col-0 |
| Genotype | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | PhyD | PhyD | PhyD | PhyD | PhyD | PhyD | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | WT | PhyD | PhyD | PhyD | PhyD | PhyD | PhyD |
| Variety | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | Col-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | WS-0 WT | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD | Col-0 PhyD |
| Radiation | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Background Earth | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation | Cosmic radiation |
| Gravity | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Terrestrial | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity | Microgravity |
| Developmental | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots | 11 day old seedling roots |
| Time.series.or.Concentration.gradient | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point | Single time point |
| Light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | 4-6 umoles m-2 s-1 total light | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown | Light-activated for 4 hours, then dark-grown |
| Analysis.Light | Alight | Alight | Alight | Dark | Dark | Dark | Alight | Alight | Alight | Dark | Dark | Dark | Alight | Alight | Alight | Dark | Dark | Dark | Alight | Alight | Alight | Dark | Dark | Dark | Alight | Alight | Alight | Dark | Dark | Dark | Alight | Alight | Alight | Dark | Dark | Dark |
| Assay..RNAseq. | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling | RNAseq Transcription Profiling |
| Temperature | ||||||||||||||||||||||||||||||||||||
| Treatment.type | ||||||||||||||||||||||||||||||||||||
| Treatment.intensity | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| Treament.timing | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| Preservation.Method. | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater | RNAlater |
readData.out_ <- readData(inFile = inputFile_,
input_missingValue = "geneMedian",
input_dataFileFormat = 1,
input_minCounts = 0.5,
input_NminSamples = 1,
input_countsLogStart = 4,
input_CountsTransform = 1)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
kable(head(readData.out_$data)) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Col0_GC_Alight_Rep1 | Col0_GC_Alight_Rep2 | Col0_GC_Alight_Rep3 | Col0_GC_dark_Rep1 | Col0_GC_dark_Rep2 | Col0_GC_dark_Rep3 | Ws_GC_Alight_Rep1 | Ws_GC_Alight_Rep2 | Ws_GC_Alight_Rep3 | Ws_GC_dark_Rep1 | Ws_GC_dark_Rep2 | Ws_GC_dark_Rep3 | Col0PhyD_GC_Alight_Rep1 | Col0PhyD_GC_Alight_Rep2 | Col0PhyD_GC_Alight_Rep3 | Col0PhyD_GC_dark_Rep1 | Col0PhyD_GC_dark_Rep2 | Col0PhyD_GC_dark_Rep3 | Col0_FLT_Alight_Rep1 | Col0_FLT_Alight_Rep2 | Col0_FLT_Alight_Rep3 | Col0_FLT_dark_Rep1 | Col0_FLT_dark_Rep2 | Col0_FLT_dark_Rep3 | Ws_FLT_Alight_Rep1 | Ws_FLT_Alight_Rep2 | Ws_FLT_Alight_Rep3 | Ws_FLT_dark_Rep1 | Ws_FLT_dark_Rep2 | Ws_FLT_dark_Rep3 | Col0PhyD_FLT_Alight_Rep1 | Col0PhyD_FLT_Alight_Rep2 | Col0PhyD_FLT_Alight_Rep3 | Col0PhyD_FLT_dark_Rep1 | Col0PhyD_FLT_dark_Rep2 | Col0PhyD_FLT_dark_Rep3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AT1G07590 | 14.09484 | 15.82643 | 16.00050 | 13.10404 | 13.51777 | 14.64386 | 13.409439 | 15.77315 | 14.82674 | 8.734572 | 12.853189 | 14.49060 | 14.30307 | 16.42429 | 16.25823 | 14.93434 | 14.023780 | 13.652232 | 13.06820 | 13.47644 | 15.85437 | 14.53257 | 12.546514 | 16.29378 | 13.70761 | 17.15691 | 15.84185 | 11.771773 | 15.17731 | 15.44411 | 15.63005 | 16.46416 | 16.53664 | 16.16638 | 14.22419 | 11.227882 |
| AT1G21310 | 13.82237 | 12.89321 | 13.59194 | 12.69178 | 11.84638 | 13.57377 | 11.609280 | 14.87147 | 13.40320 | 10.803775 | 11.668910 | 12.93797 | 12.21651 | 15.52722 | 14.64023 | 14.21443 | 13.702426 | 13.487243 | 13.86935 | 13.89505 | 14.02481 | 14.15226 | 13.295761 | 15.67999 | 14.01774 | 15.40720 | 14.53493 | 13.029378 | 14.92947 | 14.70538 | 14.61414 | 13.92094 | 14.68044 | 14.58744 | 13.84959 | 11.987155 |
| AT2G33830 | 13.00360 | 12.70793 | 13.19989 | 11.07625 | 11.44428 | 12.10500 | 11.605670 | 13.01332 | 13.23629 | 6.342765 | 8.041026 | 10.62547 | 13.12869 | 14.63728 | 13.86356 | 12.00093 | 9.571015 | 8.582187 | 13.84084 | 13.54377 | 14.43678 | 13.47765 | 11.086524 | 14.73928 | 13.32348 | 14.63064 | 13.90582 | 9.399696 | 12.33273 | 12.11427 | 14.45015 | 14.83492 | 14.74970 | 13.46973 | 13.62021 | 13.043726 |
| AT1G07610 | 12.08104 | 14.30673 | 14.23944 | 10.40031 | 11.39977 | 12.30921 | 9.268934 | 12.67998 | 11.15489 | 4.495592 | 9.733672 | 10.63264 | 11.22988 | 14.58783 | 14.11140 | 12.85493 | 11.888352 | 11.502633 | 10.64115 | 11.46993 | 13.77730 | 12.79971 | 9.080162 | 14.60663 | 10.27736 | 13.91128 | 12.58584 | 8.510065 | 11.83719 | 12.25284 | 13.89512 | 14.26232 | 14.34338 | 13.98226 | 11.39071 | 6.943481 |
| AT5G03240 | 13.20130 | 13.12291 | 13.32156 | 11.49666 | 11.57663 | 13.03192 | 12.702830 | 13.73541 | 14.16480 | 10.261950 | 10.374225 | 11.62274 | 13.07120 | 14.91590 | 14.53655 | 13.45543 | 11.608011 | 11.671690 | 12.73701 | 13.21551 | 13.74191 | 12.73065 | 10.975705 | 12.96365 | 12.31093 | 14.01545 | 13.06204 | 10.566490 | 12.53392 | 12.64681 | 13.26962 | 14.37130 | 14.68130 | 13.65752 | 12.14785 | 12.731019 |
| AT2G45960 | 14.76921 | 14.16179 | 14.40573 | 12.98223 | 12.07059 | 13.29898 | 13.412024 | 14.52394 | 14.19108 | 11.907155 | 12.186793 | 12.66575 | 12.72364 | 15.00718 | 14.16692 | 13.92458 | 12.483819 | 12.222020 | 13.90170 | 13.63596 | 14.27782 | 13.88254 | 12.522675 | 13.61255 | 14.13872 | 13.88720 | 14.02430 | 11.971582 | 13.47262 | 13.65144 | 13.90907 | 13.79361 | 14.08793 | 13.30008 | 12.89654 | 12.072701 |
readSampleInfo.out_ <- readSampleInfo(inFile = sampleInfoFile_,
readData.out = readData.out_)
kable(readSampleInfo.out_) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Gravity | Variety | Light | |
|---|---|---|---|
| Col0_GC_Alight_Rep1 | Terrestrial | Col0 WT | Alight |
| Col0_GC_Alight_Rep2 | Terrestrial | Col0 WT | Alight |
| Col0_GC_Alight_Rep3 | Terrestrial | Col0 WT | Alight |
| Col0_GC_dark_Rep1 | Terrestrial | Col0 WT | Dark |
| Col0_GC_dark_Rep2 | Terrestrial | Col0 WT | Dark |
| Col0_GC_dark_Rep3 | Terrestrial | Col0 WT | Dark |
| Ws_GC_Alight_Rep1 | Terrestrial | WS0 WT | Alight |
| Ws_GC_Alight_Rep2 | Terrestrial | WS0 WT | Alight |
| Ws_GC_Alight_Rep3 | Terrestrial | WS0 WT | Alight |
| Ws_GC_dark_Rep1 | Terrestrial | WS0 WT | Dark |
| Ws_GC_dark_Rep2 | Terrestrial | WS0 WT | Dark |
| Ws_GC_dark_Rep3 | Terrestrial | WS0 WT | Dark |
| Col0PhyD_GC_Alight_Rep1 | Terrestrial | Col0 PhyD | Alight |
| Col0PhyD_GC_Alight_Rep2 | Terrestrial | Col0 PhyD | Alight |
| Col0PhyD_GC_Alight_Rep3 | Terrestrial | Col0 PhyD | Alight |
| Col0PhyD_GC_dark_Rep1 | Terrestrial | Col0 PhyD | Dark |
| Col0PhyD_GC_dark_Rep2 | Terrestrial | Col0 PhyD | Dark |
| Col0PhyD_GC_dark_Rep3 | Terrestrial | Col0 PhyD | Dark |
| Col0_FLT_Alight_Rep1 | Microgravity | Col0 WT | Alight |
| Col0_FLT_Alight_Rep2 | Microgravity | Col0 WT | Alight |
| Col0_FLT_Alight_Rep3 | Microgravity | Col0 WT | Alight |
| Col0_FLT_dark_Rep1 | Microgravity | Col0 WT | Dark |
| Col0_FLT_dark_Rep2 | Microgravity | Col0 WT | Dark |
| Col0_FLT_dark_Rep3 | Microgravity | Col0 WT | Dark |
| Ws_FLT_Alight_Rep1 | Microgravity | WS0 WT | Alight |
| Ws_FLT_Alight_Rep2 | Microgravity | WS0 WT | Alight |
| Ws_FLT_Alight_Rep3 | Microgravity | WS0 WT | Alight |
| Ws_FLT_dark_Rep1 | Microgravity | WS0 WT | Dark |
| Ws_FLT_dark_Rep2 | Microgravity | WS0 WT | Dark |
| Ws_FLT_dark_Rep3 | Microgravity | WS0 WT | Dark |
| Col0PhyD_FLT_Alight_Rep1 | Microgravity | Col0 PhyD | Alight |
| Col0PhyD_FLT_Alight_Rep2 | Microgravity | Col0 PhyD | Alight |
| Col0PhyD_FLT_Alight_Rep3 | Microgravity | Col0 PhyD | Alight |
| Col0PhyD_FLT_dark_Rep1 | Microgravity | Col0 PhyD | Dark |
| Col0PhyD_FLT_dark_Rep2 | Microgravity | Col0 PhyD | Dark |
| Col0PhyD_FLT_dark_Rep3 | Microgravity | Col0 PhyD | Dark |
input_noIDConversion_ <- TRUE
allGeneInfo.out_ <- geneInfo(fileName = geneInfoFile_)
converted.out_ <- NULL
convertedData.out_ <- convertedData(converted.out = NULL,
readData.out = readData.out_,
input_noIDConversion = TRUE)
nGenesFilter(readData.out = readData.out_,
converted.out = NULL,
convertedData.out = convertedData.out_,
input_noIDConversion = TRUE)
## [1] "16156 genes in 36 samples. 16155 genes passed filter.\n Original gene IDs used."
convertedCounts.out_ <- convertedCounts(readData.out = readData.out_, converted.out = NULL) # converted counts, just for compatibility
# Read counts per library
parDefault_ <- par()
par(mar = c(12, 4, 2, 2))
# barplot of total read counts
rawCounts <- readData.out_$rawCounts
groups_ <- as.factor(detectGroups(colnames(rawCounts)))
if (nlevels(groups_) <= 1 | nlevels(groups_) > 20) {
col1_ <- "green"
} else {
col1_ <- rainbow(nlevels(groups_))[groups_]
}
barplot(colSums(readData.out_$rawCounts) / 1e6,
col = col1_, las = 3, main = "Total read counts (millions)"
)
readCountsBias(readData.out = readData.out_, readSampleInfo.out = readSampleInfo.out_) # detecting bias in sequencing depth
## [1] 0.05123677
## [1] 0.5460606
## [1] 0.2013552
## [1] 0.3019591
## [1] "No bias detected"
# Box plot
boxplot(
x = readData.out_$data,
las = 2, col = col1_,
ylab = "Transformed expression levels",
main = "Distribution of transformed data"
)
# Density plot
par(parDefault_)
## Warning in par(parDefault_): graphical parameter "cin" cannot be set
## Warning in par(parDefault_): graphical parameter "cra" cannot be set
## Warning in par(parDefault_): graphical parameter "csi" cannot be set
## Warning in par(parDefault_): graphical parameter "cxy" cannot be set
## Warning in par(parDefault_): graphical parameter "din" cannot be set
## Warning in par(parDefault_): graphical parameter "page" cannot be set
densityPlot(readData.out = readData.out_,
mycolors = mycolors_)
# Scatter plot of the first two samples
plot(
x = readData.out_$data[, 1:2],
xlab = colnames(readData.out_$data)[1],
ylab = colnames(readData.out_$data)[2],
main = "Scatter plot of first two samples"
)
#### plot gene or gene family
genePlot(allGeneInfo.out = allGeneInfo.out_,
convertedData.out = convertedData.out_,
input_selectOrg = "BestMatch",
input_geneSearch = "HOXA")
## NULL
geneBarPlotError(convertedData.out = convertedData.out_,
allGeneInfo.out = allGeneInfo.out_,
input_selectOrg = 'BestMatch',
input_geneSearch = "HOXA",
input_useSD = "FALSE") # Use standard deviation instead of standard error in error bar?
## NULL
# hierarchical clustering tree
x <- readData.out_$data
maxGene <- apply(x, 1, max)
# remove bottom 25% lowly expressed genes, which inflate the PPC
x <- x[which(maxGene > quantile(maxGene)[1]), ]
plot(as.dendrogram(hclust2(dist2(t(x)))), ylab = "1 - Pearson C.C.", type = "rectangle")
# Correlation matrix
#input_labelPCC_ <- TRUE # Show correlation coefficient?
correlationMatrix(readData.out = readData.out_, input_labelPCC = TRUE)
png(paste(params$input_folder, "heatmap.png", sep = "/"), width = 10, height = 15, units = "in", res = 300)
staticHeatmap(readData.out = readData.out_,
readSampleInfo.out = readSampleInfo.out_,
heatColors = heatColors_,
input_nGenes = 1000,
input_geneCentering = TRUE,
input_sampleCentering = FALSE,
input_geneNormalize = FALSE,
input_sampleNormalize = FALSE,
input_noSampleClustering = FALSE,
input_heatmapCutoff = 4,
input_distFunctions = 1,
input_hclustFunctions = 1,
input_heatColors1 = 1,
input_selectFactorsHeatmap = 'Gravity')
dev.off()
## quartz_off_screen
## 2
[heatmap] (heatmap.png)
heatmapPlotly(convertedData.out = convertedData.out_,
heatColors = heatColors_,
allGeneInfo.out = allGeneInfo.out_,
input_geneCentering = TRUE,
input_sampleCentering = FALSE,
input_geneNormalize = FALSE,
input_sampleNormalize = FALSE,
input_heatColors1 = 1)# interactive heatmap using Plotly
distributionSD(convertedData.out = convertedData.out_,
input_nGenesKNN = 2000) # Distribution of standard deviations
KmeansNclusters(convertedData.out = convertedData.out_,
input_nGenesKNN = 2000) # Number of clusters
Kmeans.out_ <- Kmeans(convertedData.out = convertedData.out_,
maxGeneClustering = 12000,
input_nGenesKNN = 2000,
input_nClusters = 4,
input_kmeansNormalization = "geneMean",
input_KmeansReRun = 0) # Running K-means
KmeansHeatmap(Kmeans.out = Kmeans.out_,
.mycolors = mycolors_,
.heatColors = heatColors_,
.input_heatColors1 = 1) # Heatmap for k-Means
# Read gene sets for enrichment analysis
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
# Alternatively, users can use their own GMT files by
# GeneSets.out_ <- readGMTRobust('somefile.GMT')
results <- KmeansGO(Kmeans.out = Kmeans.out_,
input_nClusters = 4,
GeneSets.out = GeneSets.out_) # Enrichment analysis for k-Means clusters
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Cluster | adj.Pval | Genes | Pathways |
|---|---|---|---|
| A | 2.25e-169 | 236 | Amide biosynthetic process |
| 7.51e-167 | 225 | Translation | |
| 1.76e-166 | 225 | Peptide biosynthetic process | |
| 2.01e-161 | 313 | Organonitrogen compound biosynthetic process | |
| 2.03e-159 | 242 | Cellular amide metabolic process | |
| 4.21e-158 | 226 | Peptide metabolic process | |
| 1.69e-89 | 137 | Ribonucleoprotein complex biogenesis | |
| 5.02e-83 | 206 | Cellular component biogenesis | |
| 1.94e-71 | 109 | Ribosome biogenesis | |
| 1.93e-62 | 95 | Response to cadmium ion | |
| B | 1.41e-14 | 17 | Detoxification |
| 2.90e-14 | 19 | Response to toxic substance | |
| 2.90e-14 | 16 | Drug catabolic process | |
| 2.90e-14 | 15 | Cellular oxidant detoxification | |
| 3.83e-14 | 10 | Water transport | |
| 3.83e-14 | 10 | Fluid transport | |
| 3.83e-14 | 15 | Cellular detoxification | |
| 4.93e-14 | 12 | Hydrogen peroxide catabolic process | |
| 7.79e-14 | 15 | Cellular response to toxic substance | |
| 2.35e-13 | 20 | Response to oxidative stress | |
| C | 1.83e-32 | 129 | Response to abiotic stimulus |
| 2.31e-31 | 83 | Response to inorganic substance | |
| 4.80e-25 | 54 | Response to metal ion | |
| 1.10e-23 | 88 | Cellular catabolic process | |
| 4.30e-23 | 96 | Catabolic process | |
| 5.18e-23 | 45 | Response to cadmium ion | |
| 5.68e-22 | 93 | Response to oxygen-containing compound | |
| 7.79e-21 | 87 | Oxidation-reduction process | |
| 9.68e-21 | 88 | Cellular response to chemical stimulus | |
| 6.58e-19 | 74 | Response to acid chemical | |
| D | 1.66e-16 | 51 | Response to abiotic stimulus |
| 1.66e-16 | 20 | Cellular response to decreased oxygen levels | |
| 1.66e-16 | 43 | Cellular response to chemical stimulus | |
| 1.66e-16 | 20 | Cellular response to oxygen levels | |
| 1.66e-16 | 20 | Cellular response to hypoxia | |
| 7.26e-16 | 38 | Cellular response to stress | |
| 9.22e-16 | 20 | Response to hypoxia | |
| 1.07e-15 | 20 | Response to decreased oxygen levels | |
| 1.07e-15 | 20 | Response to oxygen levels | |
| 2.58e-15 | 39 | Response to external stimulus |
tSNEgenePlot(Kmeans.out_,
input_seedTSNE = 0,
input_colorGenes = TRUE, # Color genes in t-SNE plot?
mycolors = mycolors_) # Plot genes using t-SNE
PCAplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectFactors = colnames(readSampleInfo.out_)[1],
input_selectFactors2 = colnames(readSampleInfo.out_)[2])
MDSplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectFactors = colnames(readSampleInfo.out_)[1],
input_selectFactors2 = colnames(readSampleInfo.out_)[2])
tSNEplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectFactors = colnames(readSampleInfo.out_)[1],
input_selectFactors2 = colnames(readSampleInfo.out_)[2],
input_tsneSeed2 = 0)
# Read gene sets for pathway analysis using PGSEA on principal components
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
PCApathway(convertedData.out = convertedData.out_,
GeneSets.out = GeneSets.out_) # Run PGSEA analysis
cat(
PCA2factor(readData.out = readData.out_,
readSampleInfo.out = readSampleInfo.out_)
) # The correlation between PCs with factors
##
## Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Light (p=5.73e-03).
## PC2 is correlated with Variety (p=2.38e-06).
## PC5 is correlated with Gravity (p=1.07e-05).
limma.out_ <- limma(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_dataFileFormat = 1,
input_countsLogStart = 4,
convertedCounts.out = convertedCounts.out_,
input_CountsDEGMethod = 3, # 3
input_limmaPval = 0.1,
input_limmaFC = 2,
input_selectModelComprions = paste0(colnames(readSampleInfo.out_)[1], ": ",
unique(readSampleInfo.out_[,1])[1], " vs. ",
unique(readSampleInfo.out_[,1])[2]), #"Gravity: Microgravity vs. Terrestrial", # format as "group: control vs. mutant"
input_selectFactorsModel = colnames(readSampleInfo.out_)[1], #"Gravity",
input_selectInteractions = NULL,
input_selectBlockFactorsModel = NULL,
factorReferenceLevels.out = paste(colnames(readSampleInfo.out_)[1],
unique(readSampleInfo.out_[,1])[2], sep = ":")) #"Gravity:Terrestrial")
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
DEG.data.out_ <- DEG.data(limma.out = limma.out_,
convertedData.out = convertedData.out_,
allGeneInfo.out = allGeneInfo.out_)
limma.out_$comparisons
## [1] "Terrestrial-Microgravity"
nVennGroups <- min(params$nVennGroupsMax, length(limma.out_$comparisons)) # if less than three comparisons, include all comparisons.
vennPlot(limma.out = limma.out_,
input_selectComparisonsVenn = limma.out_$comparisons[1:nVennGroups],
input_UpDownRegulated = FALSE) # Split up and down regulated genes
sigGeneStats(limma.out_) # number of DEGs as figure
sigGeneStatsTable(limma.out_) # number of DEGs as table
## Comparisons Up Down
## Terrestrial-Microgravity Terrestrial-Microgravity 50 77
# input_selectContrast_ <- "Microgravity-Terrestrial" # Selected comparisons
selectedHeatmap.data.out_ <- selectedHeatmap.data(
convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
limma.out = limma.out_,
.converted.out = NULL,
.readData.out = readData.out_,
.input_noIDConversion = TRUE,
input_dataFileFormat = 1,
input_CountsDEGMethod = 3,
input_selectModelComprions = paste0(colnames(readSampleInfo.out_)[1], ": ",
unique(readSampleInfo.out_[,1])[1], " vs. ",
unique(readSampleInfo.out_[,1])[2]), #"Gravity: Microgravity vs. Terrestrial", # format as "group: control vs. mutant"
input_selectFactorsModel = colnames(readSampleInfo.out_)[1], #"Gravity",
factorReferenceLevels.out = paste(colnames(readSampleInfo.out_)[1],
unique(readSampleInfo.out_[,1])[2], sep = ":"), #"Gravity:Terrestrial"
input_selectContrast = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-") # "Microgravity-Terrestrial"
)
selectedHeatmap(selectedHeatmap.data.out = selectedHeatmap.data.out_,
.mycolors = mycolors_,
.heatColors = heatColors_) # heatmap for DEGs in selected comparison
# Save gene lists and data into files
write.csv(
selectedHeatmap.data(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
.converted.out = converted.out_,
.readData.out = readData.out_,
.input_noIDConversion = input_noIDConversion_,
input_dataFileFormat = 1,
input_CountsDEGMethod = 3,
input_selectModelComprions = paste0(colnames(readSampleInfo.out_)[1], ": ",
unique(readSampleInfo.out_[,1])[1], " vs. ",
unique(readSampleInfo.out_[,1])[2]), #"Gravity: Microgravity vs. Terrestrial", # format as "group: control vs. mutant"
input_selectFactorsModel = colnames(readSampleInfo.out_)[1], #"Gravity",
factorReferenceLevels.out = paste(colnames(readSampleInfo.out_)[1],
unique(readSampleInfo.out_[,1])[2], sep = ":"), #"Gravity:Terrestrial"
input_selectContrast = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-"),
limma.out = limma.out_)$genes,
paste(params$input_folder, "heatmap.data.csv", sep = "/")
)
write.csv(DEG.data(limma.out = limma.out_, convertedData.out = convertedData.out_, allGeneInfo.out = allGeneInfo.out_),
paste(params$input_folder, "DEG.data.csv", sep = "/"))
write(AllGeneListsGMT(limma.out_),
paste(params$input_folder, "AllGeneListsGMT.gmt", sep = "/"))
input_selectGO2_ <- "GOBP" # Gene set category
geneListData.out_ <- geneListData(limma.out = limma.out_,
allGeneInfo.out = allGeneInfo.out_,
input_selectGO2 = "GOBP",
input_selectOrg = "NEW",
input_limmaPval = 0.1,
input_limmaFC = 2,
input_selectContrast = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-")# "Microgravity-Terrestrial"
)
volcanoPlot(limma.out = limma.out_,
input_limmaPval = 0.1,
input_limmaFC = 2,
input_selectContrast = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-")# "Microgravity-Terrestrial"
)
scatterPlot(limma.out = limma.out_,
convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_dataFileFormat = 1,
input_CountsDEGMethod = 3,
input_limmaPval = 0.1,
input_limmaFC = 2,
input_selectModelComprions = paste0(colnames(readSampleInfo.out_)[1], ": ",
unique(readSampleInfo.out_[,1])[1], " vs. ",
unique(readSampleInfo.out_[,1])[2]), #"Gravity: Microgravity vs. Terrestrial", # format as "group: control vs. mutant"
input_selectFactorsModel = colnames(readSampleInfo.out_)[1], #"Gravity",
factorReferenceLevels.out = paste(colnames(readSampleInfo.out_)[1],
unique(readSampleInfo.out_[,1])[2], sep = ":"), #"Gravity:Terrestrial"
input_selectContrast = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-"))
MAplot(
limma.out = limma.out_,
convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
.converted.out = converted.out_,
.readData.out = readData.out_,
.input_noIDConversion = TRUE,
input_dataFileFormat = 1,
input_CountsDEGMethod = 3,
input_limmaPval = 0.1,
input_limmaFC = 2,
input_selectModelComprions = paste0(colnames(readSampleInfo.out_)[1], ": ",
unique(readSampleInfo.out_[,1])[1], " vs. ",
unique(readSampleInfo.out_[,1])[2]), #"Gravity: Microgravity vs. Terrestrial", # format as "group: control vs. mutant"
input_selectFactorsModel = colnames(readSampleInfo.out_)[1], #"Gravity",
factorReferenceLevels.out = paste(colnames(readSampleInfo.out_)[1],
unique(readSampleInfo.out_[,1])[2], sep = ":"), #"Gravity:Terrestrial"
input_selectContrast = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-")
)
geneListGOTable.out_ <- geneListGOTable(GeneSets.out = GeneSets.out_,
minGenesEnrichment = 2,
selectedHeatmap.data.out = selectedHeatmap.data.out_)
# Read pathway data again
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
#input_removeRedudantSets_ <- TRUE # Remove highly redundant gene sets?
results <- geneListGO(geneListGOTable.out = geneListGOTable.out_,
input_removeRedudantSets = TRUE) # Enrichment analysis
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | adj.Pval | nGenes | Pathways |
|---|---|---|---|
| Down regulated | 2.0e-06 | 9 | Phosphorelay signal transduction system |
| 5.4e-06 | 20 | Response to organic substance | |
| 7.0e-06 | 18 | Response to endogenous stimulus | |
| 7.0e-06 | 18 | Response to hormone | |
| 1.7e-05 | 17 | Response to oxygen-containing compound | |
| 2.0e-05 | 9 | Response to organic cyclic compound | |
| 8.9e-05 | 8 | Response to antibiotic | |
| 1.4e-04 | 16 | System development | |
| 1.6e-04 | 10 | Intracellular signal transduction | |
| 1.6e-04 | 15 | Cellular response to chemical stimulus | |
| Up regulated | 3.2e-06 | 6 | Flavonoid metabolic process |
| 1.4e-05 | 16 | Response to abiotic stimulus | |
| 1.4e-05 | 5 | Flavonoid biosynthetic process | |
| 1.6e-05 | 6 | Circadian rhythm | |
| 1.6e-05 | 8 | Response to auxin | |
| 1.9e-05 | 6 | Rhythmic process | |
| 1.5e-04 | 9 | Response to light stimulus | |
| 1.8e-04 | 9 | Response to radiation | |
| 5.3e-04 | 3 | Neutral amino acid transport | |
| 5.8e-04 | 4 | Response to UV-B |
STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out_ = 10090 # mouse 10090, human 9606 etc.
STRING10_species_ <- read.csv(STRING10_speciesFile_)
ix <- grep("Arabidopsis thaliana", STRING10_species_$official_name)
findTaxonomyID.out_ <- STRING10_species_[ix, 1] # find taxonomyID
findTaxonomyID.out_
## [1] 3702
Enrichment analysis using STRING
STRINGdb_geneList.out_ <- STRINGdb_geneList(geneListData.out = geneListData.out_,
findTaxonomyID.out = findTaxonomyID.out_) # convert gene lists
## Warning: we couldn't map to STRING 0% of your identifiers
# input_STRINGdbGO_ <- "Process" #' Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro'
results <- stringDB_GO_enrichmentData(selectedHeatmap.data.out = selectedHeatmap.data.out_,
minGenesEnrichment = 2,
input_STRINGdbGO = "Process", #' Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro'
findTaxonomyID.out = findTaxonomyID.out_,
STRINGdb_geneList.out = STRINGdb_geneList.out_) # enrichment using STRING
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : methodMT parameter is depecated. Only FDR correction is available.
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : iea parameter is deprecated.
## [1] "Process"
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : methodMT parameter is depecated. Only FDR correction is available.
## Warning in string_db$get_enrichment(ids, category = input_STRINGdbGO, methodMT =
## "fdr", : iea parameter is deprecated.
## [1] "Process"
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| “No significant enrichment found.” | adj.Pval |
|---|---|
| No significant enrichment found. | NULL |
PPI network retrieval and analysis
stringDB_network1(geneLists = 1,
input_nGenesPPI = 100,
findTaxonomyID.out = findTaxonomyID.out_,
STRINGdb_geneList.out = STRINGdb_geneList.out_) # Show PPI network
Generating interactive PPI
write(
stringDB_network_link(
input_nGenesPPI = 100,
findTaxonomyID.out = findTaxonomyID.out_,
STRINGdb_geneList.out = STRINGdb_geneList.out_,
geneListData.out = geneListData.out_
),
paste(params$input_folder, "PPI_results.html", sep = "/")
)
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
## Warning: we couldn't map to STRING 0% of your identifiers
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
## Warning: 'string_db$get_link' is deprecated.
## Use 'Contact developers to request functionality' instead.
## See help("Deprecated")
# Read pathway data again
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
gagePathwayData.out_ <- gagePathwayData(limma.out = limma.out_,
input_minSetSize = 15,
input_maxSetSize = 2000,
input_selectContrast1 = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-"), #"Microgravity-Terrestrial",
input_pathwayPvalCutoff = 0.2,
input_nPathwayShow = 30,
input_absoluteFold = FALSE,
input_GenePvalCutoff = 1,
GeneSets.out = GeneSets.out_) # pathway analysis using GAGE
results <- gagePathwayData.out_ # Enrichment analysis for k-Means clusters
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GAGE analysis: Terrestrial vs Microgravity | statistic | Genes | adj.Pval |
|---|---|---|---|---|
| Down | Cellular response to decreased oxygen levels | -7.4246 | 176 | 3.4e-10 |
| Cellular response to oxygen levels | -7.4246 | 176 | 3.4e-10 | |
| Cellular response to hypoxia | -7.3912 | 175 | 3.4e-10 | |
| Response to hypoxia | -7.0149 | 197 | 1.8e-09 | |
| Response to oxygen levels | -6.991 | 201 | 1.8e-09 | |
| Response to decreased oxygen levels | -6.9904 | 200 | 1.8e-09 | |
| Root epidermal cell differentiation | -4.1893 | 115 | 5.4e-03 | |
| Plant epidermal cell differentiation | -4.0288 | 132 | 7.7e-03 | |
| Response to toxic substance | -4.0187 | 275 | 7.7e-03 | |
| Trichoblast differentiation | -3.938 | 102 | 1.1e-02 | |
| Cell maturation | -3.6762 | 96 | 2.2e-02 | |
| Trichoblast maturation | -3.6762 | 96 | 2.2e-02 | |
| Root hair cell differentiation | -3.6762 | 96 | 2.2e-02 | |
| Anatomical structure maturation | -3.5015 | 151 | 3.2e-02 | |
| Response to antibiotic | -3.4886 | 255 | 3.2e-02 | |
| Antibiotic metabolic process | -3.4875 | 188 | 3.2e-02 | |
| Antibiotic catabolic process | -3.455 | 78 | 3.8e-02 | |
| Response to drug | -3.4042 | 482 | 3.8e-02 | |
| Cellular response to toxic substance | -3.3863 | 148 | 3.8e-02 | |
| Response to reactive oxygen species | -3.3836 | 145 | 3.8e-02 | |
| Up | Photosynthesis | 5.401 | 223 | 1.1e-04 |
| Ribonucleoprotein complex biogenesis | 5.01 | 438 | 3.5e-04 | |
| RNA modification | 4.8591 | 321 | 4.9e-04 | |
| Ribosome biogenesis | 4.7339 | 343 | 7.0e-04 | |
| NcRNA metabolic process | 4.2086 | 425 | 5.6e-03 | |
| NcRNA processing | 4.1143 | 357 | 7.2e-03 | |
| Cellular response to DNA damage stimulus | 3.7807 | 337 | 2.3e-02 | |
| RRNA processing | 3.7162 | 239 | 2.5e-02 | |
| DNA repair | 3.7091 | 314 | 2.5e-02 | |
| RRNA metabolic process | 3.6563 | 244 | 2.8e-02 |
pathwayListData.out_ <- pathwayListData(allGeneInfo.out = allGeneInfo.out_,
input_selectOrg = "NEW",
input_selectGO = "GOBP",
input_pathwayMethod = 1,
gagePathwayData.out = gagePathwayData.out_,
fgseaPathwayData.out = fgseaPathwayData.out_,
GeneSets.out = GeneSets.out_)
enrichmentPlot(pathwayListData.out_, enrichedTerms = 25)
## NULL
enrichmentNetwork(pathwayListData.out_)
enrichmentNetworkPlotly(pathwayListData.out_)
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
# input_pathwayMethod_ <- 3 # 1 fgsea
fgseaPathwayData.out_ <- fgseaPathwayData(limma.out = limma.out_,
input_minSetSize = 15,
input_maxSetSize = 2000,
input_selectContrast1 = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-"),
input_pathwayPvalCutoff = 0.2,
input_absoluteFold = FALSE,
input_nPathwayShow = 30,
input_GenePvalCutoff = 1,
GeneSets.out = GeneSets.out_) # Pathway analysis using fgsea
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
results <- fgseaPathwayData.out_ # Enrichment analysis for k-Means clusters
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| Direction | GSEA analysis: Terrestrial vs Microgravity | NES | Genes | adj.Pval |
|---|---|---|---|---|
| Down | Cellular response to decreased oxygen levels | -2.4389 | 176 | 6.8e-03 |
| Cellular response to oxygen levels | -2.4389 | 176 | 6.8e-03 | |
| Cellular response to hypoxia | -2.4354 | 175 | 6.8e-03 | |
| Response to hypoxia | -2.351 | 197 | 6.8e-03 | |
| Response to decreased oxygen levels | -2.3453 | 200 | 6.8e-03 | |
| Response to oxygen levels | -2.3434 | 201 | 6.8e-03 | |
| Trichoblast differentiation | -2.2099 | 102 | 6.8e-03 | |
| Root epidermal cell differentiation | -2.2039 | 115 | 6.8e-03 | |
| Plant epidermal cell differentiation | -2.1582 | 132 | 6.8e-03 | |
| Cell maturation | -2.1498 | 96 | 6.8e-03 | |
| Trichoblast maturation | -2.1498 | 96 | 6.8e-03 | |
| Root hair cell differentiation | -2.1498 | 96 | 6.8e-03 | |
| Root hair cell development | -2.0882 | 78 | 6.8e-03 | |
| Root hair elongation | -2.0431 | 58 | 8.7e-03 | |
| Response to hydrogen peroxide | -1.9985 | 65 | 8.7e-03 | |
| Indole glucosinolate metabolic process | -1.9908 | 27 | 8.7e-03 | |
| Up | Photosynthesis | 2.3365 | 223 | 6.8e-03 |
| Flavonoid metabolic process | 2.2952 | 68 | 6.8e-03 | |
| Flavonoid biosynthetic process | 2.2846 | 50 | 6.8e-03 | |
| Response to UV-B | 2.2808 | 59 | 6.8e-03 | |
| Response to UV | 2.186 | 95 | 6.8e-03 | |
| Regulation of flavonoid biosynthetic process | 2.1376 | 16 | 6.8e-03 | |
| Regulation of anthocyanin metabolic process | 2.046 | 19 | 1.3e-02 | |
| Response to gravity | 2.0446 | 78 | 6.8e-03 | |
| Photosynthesis, light reaction | 2.0175 | 119 | 6.8e-03 | |
| Anthocyanin-containing compound metabolic process | 1.9915 | 35 | 2.2e-02 | |
| Response to far red light | 1.9865 | 45 | 2.2e-02 | |
| Regulation of response to red or far red light | 1.9849 | 34 | 2.2e-02 | |
| Anthocyanin-containing compound biosynthetic process | 1.984 | 20 | 2.2e-02 | |
| Response to red light | 1.9683 | 55 | 1.9e-02 |
pathwayListData.out_ <- pathwayListData(allGeneInfo.out = allGeneInfo.out_,
input_selectOrg = "NEW",
input_selectGO = "GOBP",
input_pathwayMethod = 3,
gagePathwayData.out = gagePathwayData.out_,
fgseaPathwayData.out = fgseaPathwayData.out_,
GeneSets.out = GeneSets.out_)
enrichmentPlot(enrichedTerms = pathwayListData.out_,
rightMargin = 25,
mycolors = mycolors_)
enrichmentNetwork(pathwayListData.out_)
enrichmentNetworkPlotly(pathwayListData.out_)
PGSEAplot(convertedData.out = convertedData.out_,
readSampleInfo.out = readSampleInfo.out_,
input_selectOrg = "NEW",
input_dataFileFormat = 1,
input_selectGO = "GOBP",
input_minSetSize = 15,
input_maxSetSize = 2000,
input_CountsDEGMethod = 3,
input_selectModelComprions = paste0(colnames(readSampleInfo.out_)[1], ": ",
unique(readSampleInfo.out_[,1])[1], " vs. ",
unique(readSampleInfo.out_[,1])[2]), #"Gravity: Microgravity vs. Terrestrial", # format as "group: control vs. mutant"
input_selectFactorsModel = colnames(readSampleInfo.out_)[1], #"Gravity",
factorReferenceLevels.out = paste(colnames(readSampleInfo.out_)[1],
unique(readSampleInfo.out_[,1])[2], sep = ":"), #"Gravity:Terrestrial"
input_selectContrast1 = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = ":"),
input_pathwayPvalCutoff = 0.2,
input_nPathwayShow = 30,
GeneSets.out = GeneSets.out_) # pathway analysis using PGSEA
##
## Computing P values using ANOVA
#input_selectContrast2_ <- "Microgravity-Terrestrial" # select Comparison
# input_selectContrast2 = limma.out_$comparisons[3] # manually set
#input_limmaPvalViz_ <- 0.1 # FDR to filter genes
#input_limmaFCViz_ <- 2 # FDR to filter genes
genomePlotly(limma.out = limma.out_,
allGeneInfo.out = allGeneInfo.out_,
input_selectContrast2 = paste(unique(readSampleInfo.out_[,1])[1],
unique(readSampleInfo.out_[,1])[2],
sep = "-"),
input_limmaPvalViz = 0.1,
input_limmaFCViz = 2) # shows fold-changes on the genome
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(limma.out = limma.out_, allGeneInfo.out =
## allGeneInfo.out_, : NAs introduced by coercion
biclustering.out_ <- biclustering(convertedData.out = convertedData.out_,
input_nGenesBiclust = 1000,
input_biclustMethod = "BCCC()") # run analysis
input_selectBicluster_ <- 1 # select a cluster
biclustHeatmap(biclustering.out = biclustering.out_,
heatColors = heatColors_,
input_heatColors1 = 1,
input_selectBicluster = 1) # heatmap for selected cluster
#input_selectGO4_ <- "GOBP" # Gene set category
# Read pathway data again
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
results <- geneListBclustGO(minGenesEnrichment = 2,
input_selectBicluster = 1,
biclustering.out = biclustering.out_,
GeneSets.out = GeneSets.out_) # Enrichment analysis for k-Means clusters
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.9e-120 | 192 | Peptide metabolic process |
| 1.9e-117 | 182 | Translation |
| 3.3e-117 | 182 | Peptide biosynthetic process |
| 8.8e-113 | 185 | Amide biosynthetic process |
| 2.1e-111 | 196 | Cellular amide metabolic process |
| 5.5e-96 | 238 | Organonitrogen compound biosynthetic process |
| 1.4e-68 | 151 | Response to inorganic substance |
| 6.0e-57 | 101 | Response to metal ion |
| 1.3e-53 | 86 | Response to cadmium ion |
| 4.4e-49 | 197 | Response to abiotic stimulus |
wgcna.out_ <- wgcna(convertedData.out = convertedData.out_,
maxGeneWGCNA = 2000,
input_mySoftPower = 5,
input_nGenesNetwork = 1000,
input_minModuleSize = 20) # run WGCNA
## Warning: executing %dopar% sequentially: no parallel backend registered
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.7780 1.540 0.918 368.00 378.00 520.0
## 2 2 0.2370 0.285 0.681 189.00 190.00 333.0
## 3 3 0.0936 -0.126 0.651 114.00 108.00 234.0
## 4 4 0.5360 -0.387 0.797 74.60 67.20 176.0
## 5 5 0.7270 -0.553 0.905 52.10 44.20 140.0
## 6 6 0.8430 -0.724 0.971 38.10 30.50 116.0
## 7 7 0.8550 -0.824 0.955 28.80 21.40 98.1
## 8 8 0.9000 -0.922 0.984 22.40 15.90 84.7
## 9 9 0.9090 -0.995 0.969 17.80 12.00 74.2
## 10 10 0.9080 -1.060 0.953 14.40 9.25 65.6
## 11 12 0.9290 -1.130 0.950 9.86 5.67 52.5
## 12 14 0.9370 -1.180 0.941 7.06 3.53 43.0
## 13 16 0.9550 -1.210 0.949 5.24 2.34 35.8
## 14 18 0.9530 -1.220 0.940 4.00 1.54 30.2
## 15 20 0.9730 -1.230 0.965 3.12 1.08 25.8
## TOM calculation: adjacency..
## ..will not use multithreading.
## Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
softPower(wgcna.out_) # soft power curve
listWGCNA.Modules.out <- listWGCNA.Modules(wgcna.out_) # modules
modulePlot(wgcna.out_) # plot modules
# Read pathway data again
GeneSets.out_ <- readGeneSets(
fileName = geneSetFile_,
convertedData = convertedData.out_,
GO = "GOBP",
selectOrg = "NEW",
myrange = c(15, 2000)
)
moduleNetwork(wgcna.out = wgcna.out_,
input_noIDConversion = TRUE,
allGeneInfo.out = allGeneInfo.out_,
input_selectOrg = "NEW",
input_selectWGCNA.Module = "Entire network",
input_topGenesNetwork = 10,
input_edgeThreshold = 0.4,
input_selectGO5 = "GOBP") # show network of top genes in selected module
## softConnectivity: FYI: connecitivty of genes with less than 12 valid samples will be returned as NA.
## ..calculating connectivities..
results <- networkModuleGO(GeneSets.out = GeneSets.out_,
minGenesEnrichment = 2,
input_selectWGCNA.Module = "Entire network",
wgcna.out = wgcna.out_) # Enrichment analysis of selected module
results$adj.Pval <- format(results$adj.Pval, digits = 3)
kable(results, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover")) %>%
scroll_box(width = "100%")
| adj.Pval | Genes | Pathways |
|---|---|---|
| 1.9e-120 | 192 | Peptide metabolic process |
| 1.9e-117 | 182 | Translation |
| 3.3e-117 | 182 | Peptide biosynthetic process |
| 8.8e-113 | 185 | Amide biosynthetic process |
| 2.1e-111 | 196 | Cellular amide metabolic process |
| 5.5e-96 | 238 | Organonitrogen compound biosynthetic process |
| 1.4e-68 | 151 | Response to inorganic substance |
| 6.0e-57 | 101 | Response to metal ion |
| 1.3e-53 | 86 | Response to cadmium ion |
| 4.4e-49 | 197 | Response to abiotic stimulus |